Open Access

ARTICLE

Advanced Community Identification Model for Social Networks

by Farhan Amin¹, Jin-Ghoo Choi², Gyu Sang Choi^2,*

1 Department of Computer Engineering, Gachon University, Gyeonggi-do, 13120, Korea
2 Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, 38541, Korea

* Corresponding Author: Gyu Sang Choi. Email:

(This article belongs to the Special Issue: Big Data Analytics and Artificial Intelligence Techniques for Complex Systems)

Computers, Materials & Continua 2021, 69(2), 1687-1707. https://doi.org/10.32604/cmc.2021.017870

Received 15 February 2021; Accepted 10 April 2021; Issue published 21 July 2021

Abstract

Community detection in social networks is a hard problem because of the size, and the need of a deep understanding of network structure and functions. While several methods with significant effort in this direction have been devised, an outstanding open problem is the unknown number of communities, it is generally believed that the role of influential nodes that are surrounded by neighbors is very important. In addition, the similarity among nodes inside the same cluster is greater than among nodes from other clusters. Lately, the global and local methods of community detection have been getting more attention. Therefore, in this study, we propose an advanced community-detection model for social networks in order to identify network communities based on global and local information. Our proposed model initially detects the most influential nodes by using an Eigen score then performs local expansion powered by label propagation. This process is conducted with the same color till nodes reach maximum similarity. Finally, the communities are formed, and a clear community graph is displayed to the user. Our proposed model is completely parameter-free, and therefore, no prior information is required, such as the number of communities, etc. We perform simulations and experiments using well-known synthetic and real network benchmarks, and compare them with well-known state-of-the-art models. The results prove that our model is efficient in all aspects, because it quickly identifies communities in the network. Moreover, it can easily be used for friendship recommendations or in business recommendation systems.

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Keywords

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